Cache-Assisted Mobile-Edge Computing Over Space-Air-Ground Integrated Networks for Extended Reality Applications

Extended reality-enabled Internet of Things (XRI) provides new user experiences and a sense of immersion by adding virtual elements to the real world through Internet of Things (IoT) devices and emerging sixth-generation (6G) technologies. However, computational-intensive XRI tasks are challenging for energy-constrained small-size XRI devices to cope with, and moreover certain data require centralized computing that needs to be shared among users. To this end, we propose a cache-assisted space-air-ground integrated network mobile-edge computing (SAGIN-MEC) system for XRI applications consisting of two types of edge servers mounted on an unmanned aerial vehicle (UAV) and low-Earth orbit (LEO) satellite equipped with a cache and multiple ground XRI devices. For system efficiency, four different offloading procedures of XRI data are considered according to the type of information, i.e., shared data and private data, as well as the offloading decision and the caching status. Specifically, private data can be offloaded to either UAV or LEO satellite, while the offloading decision of shared data to the LEO satellite can be determined by the caching status. With the aim of maximizing the energy efficiency of the overall system, we jointly optimize UAV trajectory, resource allocation, and offloading decisions under latency constraints and UAV's operational limitations by using the alternating optimization (AO)-based method along with the Dinkelbach algorithm and successive convex approximation (SCA). Via numerical results, the proposed algorithm is verified to have superior performance compared to conventional partial optimizations or processes without a cache.